April 15th traditionally marks Tax Day in the United States (July 15th in 2020) and global tax evasion is big business.

Between 2008-2010, the U.S. Internal Revenue Service (IRS) identified a total tax gap of $458,000,000,000. That’s $458 Billion USD! Eighty-five percent (85%), or $387B, is due to underreporting and six percent (6%), $32B, is due to non-filing.

It’s no wonder that, in an effort to combat offshore tax evasion in 2010, the United States enacted the Foreign Account Tax Compliance Act (FATCA).

This federal law requires financial institutions outside of the United States to identify customers with a connection to the United States, including birth and current or prior residency, and report the identities and their assets to the United States Department of the Treasury. In 2014, further financial regulations saw the Common Reporting Standard extended to bank accounts on a global level. To date, more than 100 countries have agreed to these laws, mitigating tax evasion on a global scale.

Global Demands for Financial Regulations

Banks have no choice but to comply, risking being frozen out of global markets. Yet many financial institutions are struggling to scale conformity with constantly evolving global reporting and compliance laws.

Nordea, a globally oriented multinational bank with strong Nordic roots, understands that you cannot apply a linear solution to an exponential problem. Classical financial solutions of people and paper can no longer keep pace. Instead, Nordea applies cognitive automation to keep pace with the increasing regulations and consumer expectations. Cognitive automation is the application of artificial intelligence and machine learning to automate repetitive tasks that require increasing cognition beyond RPA (Robotic Process Automation), relying on high human involvement.

Nordea Bank By The Numbers

11M

customers

20

countries with Nordea presence

29,500

employees

Answers and Outcomes

Nordea sees cognitive automation, using Teradata Vantage™, as the solution to exponential problems banks are experiencing.

Vantage’s built-in Machine Learning Engine allows Nordea to:

Identify and automate key features of data that accurately predict reportable customers across their 11M customer base.

Discover and learn about new, reportable customers or fraud schemes.

Enable a broader community within the bank to contribute by integrating into the current tax reporting ecosystem.

David Rassool

Head of Automation

David Rassool is head of automation for finance and treasury reporting at Nordea Sweden. Having joined Nordea in 2010, prior roles at Nordea include head of business data science and head of data architecture. His background includes advanced analytics and enterprise information systems.

Cognitive automation forregulatory compliance

“Automation is about using data to get to the goal with as little human intervention as possible.”

David Rassool, head of automation

FATCA and CRS: the opportunity for automation

Ensuring timely reporting of US IRS tax requirements for foreign financial assets is a time and man-power intensive task for a bank.

Most financial institutions employ dozens of full-time employees whose sole job is a repetitive, circular flow across multiple platforms, technologies, and teams in order to flag customers that require mandatory reporting according to these requirements, namely FATCA and CRS.

The heaviest burden in operating a compliance process, where bank employees check every reportable customer, is to confirm the reporting classification is correct—to the tune of many thousands of customers each day. These bank employees also conduct spot-check on non-reportable customers for due diligence. These spot-checks result in several hundred customers out of millions and millions that need to be efficiently filtered. The equivalent to finding a needle in a haystack.

Human error increases the probability for false positives and false negatives. Compound this human error with increasing customer expectations and increasing requirements on privacy, regulation, tax reporting, and financial crimes and it is hard to expect banks to keep track of, and maintain high accuracy against, all of these new requirements.

The reputational and operational risk for improperly classifying a customer to the IRS is significant.

A customer who is audited at the fault of their bank’s own human error is a costly consequence; leading to eroding customer trust, closure of accounts, and losses in total assets for the bank. Alternatively, the bank must identify potential fraud for reportable customers that are deliberately not reporting.

Therefore, placing rules into analytical models in the form of automation is an easier approach.

Automation Removes Manual Process Burden

“A significant portion of processes that we run today involve some kind of manual step in them. The business case for automation is if we can look at a process and remove that burden from the people working at Nordea, then we have an opportunity to do it quicker, to do it more accurately, and we free up time for our workers to be able to actually contribute in more cognitively challenging areas.”

With 11M customers in 20 countries, Nordea uses the Machine Learning Engine in Vantage to create a machine learning-based classification that predicts customers as reportable (the dependent variable).

The ML classification compliments a traditional rules-based classification which relies on approximately 70 unique features (independent variables) to classify and flag reportable customers. Typically, a quantity of number from 0 to 1 (positive one) and 0 to -1 (negative one) shows the importance of a variable.

Common examples of the features may include:

Tax IDs and Tax Forms

Social Security Numbers and Passports

FICO Scores and Credit Forms

Place of Birth

Current or Prior Country of Residency

Volume and Frequency of Transactions

Transaction Origination and Destination

Deposits and Withdrawals

The prediction and flagging of reportable customers is ordered by the magnitude of the probability in order for the back-office to review hundreds of records. This is down from many, many thousands of records.

Moreover, combining and evaluating features for millions of customers demands the ability to scale, which Teradata Vantage enables. Traditionally, data would be ingested and pre-processed on write which means that some data would be considered superfluous and thrown away. However, Vantage ingests all of the data while applying schema when the record is read.

This means that no data is left behind or considered garbage prior to ingestion and reading.

The benefits are that the more data that may be ingested, the more accurate predictions and answers may be, as well as only discarding data when deemed invaluable.

Nordea uses native Vantage filtering and sorting techniques to remove the superfluous data and only make the valued data available for machine learning analysis. Nordea data scientists continue to do analysis, visualization, and operationalization all in one platform.

Cognitive automation reduces Nordea’s back-office processes costs by approximately $5.6M USD. A cost-savings that may be reinvested into other areas of Nordea’s automation efforts.

"Before implementing machine learning, Nordea had to organize a lot of people and prepare for performing manual validation of the FATCA and CRS reports. It's quite a labor intensive task. If we can prioritize the list of customers where we believe there may be an error in the classification then that makes a much lighter-weight back-office process to validate and know the report is correct. We also have a secondary benefit that we can actually scan the millions of customers that we have and make sure that we also haven't missed someone."

Staying One Step Ahead of Fraudsters

Employees once assigned to highly repetitive and time intensive tasks are now assigned to strategic, higher value efforts that require a higher cognition – such as mitigating risks from tax evaders and other financial crime fraudsters.

Vantage supports their efforts to keep pace with rapidly emerging and evolving financial crime tactics. Their machine learning model iterates rapidly by evolving the attributes over time. This becomes a constantly evolving game of cat-and-mouse between fraudsters and Nordea’s machine learning model. The more data you bring from any source, the Teradata Vantage platform will scale!

As financial institutions face diverse challenges when it comes to successfully combating fraud, money laundering, and cybercrime, Nordea realizes that the only way to combat the rise in sophisticated financial crimes is to leverage advanced analytics techniques and emerging practices.

Simply put, the fallout is too costly with hefty regulatory fines, loss in consumer trust, and stolen assets at stake.

Data as an asset

By running predictions on all of its customers, Nordea has become a bank built on data. With a complete picture of its customers, the broader community within the bank contributes to the overall tax reporting system by meeting increasing financial regulations, avoiding costly fines, and exceeding consumer expectations.

Turning its data into an asset, cognitive automation using Teradata Vantage provides Nordea with the critical business processes for fast, accurate, and cost-effective solutions to their toughest business challenges.